Li Jiang

The paper proposes Arbiter-K, a Governance-First execution architecture that treats LLMs as probabilistic units encapsulated by a deterministic kernel, significantly improving the security and reliability of agentic AI systems.

The paper introduces a semantic validation framework that uses unpackers as executable contracts to detect and repair semantic bugs in packer identification tools, significantly improving the reliability of malware analysis.

LoopFM proposes a novel framework to significantly improve knowledge distillation for recommendation systems by structuring the rich intermediate embeddings of large foundation models as input features, thereby overcoming the limitations of single-scalar prediction transfer.

The paper introduces COSM, a cooperative scheduling framework to facilitate concurrent operation of Processing-in-Memory (PIM) and CPU tasks on mobile platforms, improving PIM throughput by up to 2.8x with less than 2.0% CPU performance loss.